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PSL
Developer(s)Google Brain Team[1]
Initial releaseNovember 9, 2015; 9 years ago (2015-11-09)
Stable release
2.2.2[2] / May 6, 2020; 5 years ago (2020-05-06)
Repositorygithub.com/tensorflow/tensorflow
Written inPython, C++, CUDA
PlatformLinux, macOS, Windows, Android, JavaScript[3]
TypeMachine learning library
LicenseApache License 2.0
Websitewww.tensorflow.org


Probabilistic soft logic (PSL) is a SRL framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0,1].[4]

Description

In recent years there has been a rise in the approaches that combine graphical models and first-order logic to allow the development of complex probabilistic models with relational structures. A notable example of such approaches is Markov logic networks (MLNs).[5] Like MLNs PSL is a modelling language (with an accompanying implementation[6]) for learning and predicting in relational domains. Unlike MLNs, PSL uses soft truth values for predicates in an interval between [0,1]. This allows for the underlying inference to be solved quickly as a convex optimization problem. This is useful in problems such as collective classification, link prediction, social network modelling, and object identification/entity resolution/record linkage.

See also

References

  1. ^ Cite error: The named reference Credits was invoked but never defined (see the help page).
  2. ^ "PSL".
  3. ^ Cite error: The named reference js was invoked but never defined (see the help page).
  4. ^ Bach, Stephen; Broecheler, Matthias; Huang, Bert; Getoor, Lise (2017). "Hinge-Loss Markov Random Fields and Probabilistic Soft Logic". Journal of Machine Learning Research. 18: 1–67.
  5. ^ Getoor, Lise; Taskar, Ben (October 12, 2007). Introduction to Statistical Relational Learning. MIT Press. ISBN 0262072882.
  6. ^ "GitHub repository". Retrieved March 26, 2018.

Category:Bayesian statistics Category:Markov networks